{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Source: https://www.kaggle.com/worldbank/world-development-indicators\n",
    "# Folder: 'world-development-indicators'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><p style=\"font-family: Arial; font-size:3.75em;color:purple; font-style:bold\">\n",
    "Matplotlib: Exploring <br> <br> <br>Data Visualization</p><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><center><h1 style=\"font-size:2em;color:#2467C0\">World Development Indicators</h1></center>\n",
    "<br>\n",
    "<table>\n",
    "<col width=\"550\">\n",
    "<col width=\"450\">\n",
    "<tr>\n",
    "<td><img src=\"https://upload.wikimedia.org/wikipedia/commons/4/46/North_South_divide.svg\" align=\"middle\" style=\"width:550px;height:360px;\"/></td>\n",
    "<td>\n",
    "This week, we will be using an open dataset from <a href=\"https://www.kaggle.com\">Kaggle</a>. It is  <a href=\"https://www.kaggle.com/worldbank/world-development-indicators\">The World Development Indicators</a> dataset obtained from the World Bank containing over a thousand annual indicators of economic development from hundreds of countries around the world.\n",
    "<br>\n",
    "<br>\n",
    "This is a slightly modified version of the original dataset from <a href=\"http://data.worldbank.org/data-catalog/world-development-indicators\">The World Bank</a>\n",
    "<br>\n",
    "<br>\n",
    "List of the <a href=\"https://www.kaggle.com/benhamner/d/worldbank/world-development-indicators/indicators-in-data\">available indicators</a> and a <a href=\"https://www.kaggle.com/benhamner/d/worldbank/world-development-indicators/countries-in-the-wdi-data\">list of the available countries</a>.\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Initial exploration of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5656458, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./world-development-indicators/Indicators.csv')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a really large dataset, at least in terms of the number of rows.  But with 6 columns, what does this hold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
       "      <td>SP.ADO.TFRT</td>\n",
       "      <td>1960</td>\n",
       "      <td>1.335609e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio (% of working-age populat...</td>\n",
       "      <td>SP.POP.DPND</td>\n",
       "      <td>1960</td>\n",
       "      <td>8.779760e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, old (% of working-age po...</td>\n",
       "      <td>SP.POP.DPND.OL</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.634579e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, young (% of working-age ...</td>\n",
       "      <td>SP.POP.DPND.YG</td>\n",
       "      <td>1960</td>\n",
       "      <td>8.102333e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms exports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.XPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>3.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms imports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.MPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.380000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Birth rate, crude (per 1,000 people)</td>\n",
       "      <td>SP.DYN.CBRT.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>4.769789e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (kt)</td>\n",
       "      <td>EN.ATM.CO2E.KT</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.956399e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.439635e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions from gaseous fuel consumption (%...</td>\n",
       "      <td>EN.ATM.CO2E.GF.ZS</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.041292e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName CountryCode                                      IndicatorName  \\\n",
       "0  Arab World         ARB  Adolescent fertility rate (births per 1,000 wo...   \n",
       "1  Arab World         ARB  Age dependency ratio (% of working-age populat...   \n",
       "2  Arab World         ARB  Age dependency ratio, old (% of working-age po...   \n",
       "3  Arab World         ARB  Age dependency ratio, young (% of working-age ...   \n",
       "4  Arab World         ARB        Arms exports (SIPRI trend indicator values)   \n",
       "5  Arab World         ARB        Arms imports (SIPRI trend indicator values)   \n",
       "6  Arab World         ARB               Birth rate, crude (per 1,000 people)   \n",
       "7  Arab World         ARB                                 CO2 emissions (kt)   \n",
       "8  Arab World         ARB             CO2 emissions (metric tons per capita)   \n",
       "9  Arab World         ARB  CO2 emissions from gaseous fuel consumption (%...   \n",
       "\n",
       "       IndicatorCode  Year         Value  \n",
       "0        SP.ADO.TFRT  1960  1.335609e+02  \n",
       "1        SP.POP.DPND  1960  8.779760e+01  \n",
       "2     SP.POP.DPND.OL  1960  6.634579e+00  \n",
       "3     SP.POP.DPND.YG  1960  8.102333e+01  \n",
       "4     MS.MIL.XPRT.KD  1960  3.000000e+06  \n",
       "5     MS.MIL.MPRT.KD  1960  5.380000e+08  \n",
       "6     SP.DYN.CBRT.IN  1960  4.769789e+01  \n",
       "7     EN.ATM.CO2E.KT  1960  5.956399e+04  \n",
       "8     EN.ATM.CO2E.PC  1960  6.439635e-01  \n",
       "9  EN.ATM.CO2E.GF.ZS  1960  5.041292e+00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like it has different indicators for different countries with the year and value of the indicator. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many UNIQUE country names are there ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = data['CountryName'].unique().tolist()\n",
    "len(countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are there same number of country codes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many unique country codes are there ? (should be the same #)\n",
    "countryCodes = data['CountryCode'].unique().tolist()\n",
    "len(countryCodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are there many indicators or few ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1344"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many unique indicators are there ? (should be the same #)\n",
    "indicators = data['IndicatorName'].unique().tolist()\n",
    "len(indicators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many years of data do we have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many years of data do we have ?\n",
    "years = data['Year'].unique().tolist()\n",
    "len(years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the range of years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1960  to  2015\n"
     ]
    }
   ],
   "source": [
    "print(min(years),\" to \",max(years))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.5em;color:blue; font-style:bold\">\n",
    "Matplotlib: Basic Plotting, Part 1</p><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets pick a country and an indicator to explore: CO2 Emissions per capita and the USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select CO2 emissions for the United States\n",
    "hist_indicator = 'CO2 emissions \\(metric'\n",
    "hist_country = 'USA'\n",
    "\n",
    "mask1 = data['IndicatorName'].str.contains(hist_indicator) \n",
    "mask2 = data['CountryCode'].str.contains(hist_country)\n",
    "\n",
    "# stage is just those indicators matching the USA for country code and CO2 emissions over time.\n",
    "stage = data[mask1 & mask2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>22232</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1960</td>\n",
       "      <td>15.999779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48708</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1961</td>\n",
       "      <td>15.681256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77087</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1962</td>\n",
       "      <td>16.013937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105704</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1963</td>\n",
       "      <td>16.482762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134742</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1964</td>\n",
       "      <td>16.968119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CountryName CountryCode                           IndicatorName  \\\n",
       "22232   United States         USA  CO2 emissions (metric tons per capita)   \n",
       "48708   United States         USA  CO2 emissions (metric tons per capita)   \n",
       "77087   United States         USA  CO2 emissions (metric tons per capita)   \n",
       "105704  United States         USA  CO2 emissions (metric tons per capita)   \n",
       "134742  United States         USA  CO2 emissions (metric tons per capita)   \n",
       "\n",
       "         IndicatorCode  Year      Value  \n",
       "22232   EN.ATM.CO2E.PC  1960  15.999779  \n",
       "48708   EN.ATM.CO2E.PC  1961  15.681256  \n",
       "77087   EN.ATM.CO2E.PC  1962  16.013937  \n",
       "105704  EN.ATM.CO2E.PC  1963  16.482762  \n",
       "134742  EN.ATM.CO2E.PC  1964  16.968119  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see how emissions have changed over time using MatplotLib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the years\n",
    "years = stage['Year'].values\n",
    "# get the values \n",
    "co2 = stage['Value'].values\n",
    "\n",
    "# create\n",
    "plt.bar(years,co2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turns out emissions per capita have dropped a bit over time, but let's make this graphic a bit more appealing before we continue to explore it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# switch to a line plot\n",
    "plt.plot(stage['Year'].values, stage['Value'].values)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(stage['IndicatorName'].iloc[0])\n",
    "\n",
    "#label the figure\n",
    "plt.title('CO2 Emissions in USA')\n",
    "\n",
    "# to make more honest, start they y axis at 0\n",
    "plt.axis([1959, 2011,0,25])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Histograms to explore the distribution of values\n",
    "We could also visualize this data as a histogram to better explore the ranges of values in CO2 production per year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you want to just include those within one standard deviation fo the mean, you could do the following\n",
    "# lower = stage['Value'].mean() - stage['Value'].std()\n",
    "# upper = stage['Value'].mean() + stage['Value'].std()\n",
    "# hist_data = [x for x in stage[:10000]['Value'] if x>lower and x<upper ]\n",
    "\n",
    "# Otherwise, let's look at all the data\n",
    "hist_data = stage['Value'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52\n"
     ]
    }
   ],
   "source": [
    "print(len(hist_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\mars\\anaconda3\\envs\\name1gpu\\lib\\site-packages\\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the histogram of the data\n",
    "plt.hist(hist_data, 10, normed=False, facecolor='green')\n",
    "\n",
    "plt.xlabel(stage['IndicatorName'].iloc[0])\n",
    "plt.ylabel('# of Years')\n",
    "plt.title('Histogram Example')\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the USA has many years where it produced between 19-20 metric tons per capita with outliers on either side."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But how do the USA's numbers relate to those of other countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5026275</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>2011</td>\n",
       "      <td>4.724500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5026788</td>\n",
       "      <td>Caribbean small states</td>\n",
       "      <td>CSS</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>2011</td>\n",
       "      <td>9.692960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5027295</td>\n",
       "      <td>Central Europe and the Baltics</td>\n",
       "      <td>CEB</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.911131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5027870</td>\n",
       "      <td>East Asia &amp; Pacific (all income levels)</td>\n",
       "      <td>EAS</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.859548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5028456</td>\n",
       "      <td>East Asia &amp; Pacific (developing only)</td>\n",
       "      <td>EAP</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.302499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     CountryName CountryCode  \\\n",
       "5026275                               Arab World         ARB   \n",
       "5026788                   Caribbean small states         CSS   \n",
       "5027295           Central Europe and the Baltics         CEB   \n",
       "5027870  East Asia & Pacific (all income levels)         EAS   \n",
       "5028456    East Asia & Pacific (developing only)         EAP   \n",
       "\n",
       "                                  IndicatorName   IndicatorCode  Year  \\\n",
       "5026275  CO2 emissions (metric tons per capita)  EN.ATM.CO2E.PC  2011   \n",
       "5026788  CO2 emissions (metric tons per capita)  EN.ATM.CO2E.PC  2011   \n",
       "5027295  CO2 emissions (metric tons per capita)  EN.ATM.CO2E.PC  2011   \n",
       "5027870  CO2 emissions (metric tons per capita)  EN.ATM.CO2E.PC  2011   \n",
       "5028456  CO2 emissions (metric tons per capita)  EN.ATM.CO2E.PC  2011   \n",
       "\n",
       "            Value  \n",
       "5026275  4.724500  \n",
       "5026788  9.692960  \n",
       "5027295  6.911131  \n",
       "5027870  5.859548  \n",
       "5028456  5.302499  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select CO2 emissions for all countries in 2011\n",
    "hist_indicator = 'CO2 emissions \\(metric'\n",
    "hist_year = 2011\n",
    "\n",
    "mask1 = data['IndicatorName'].str.contains(hist_indicator) \n",
    "mask2 = data['Year'].isin([hist_year])\n",
    "\n",
    "# apply our mask\n",
    "co2_2011 = data[mask1 & mask2]\n",
    "co2_2011.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For how many countries do we have CO2 per capita emissions data in 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "232\n"
     ]
    }
   ],
   "source": [
    "print(len(co2_2011))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\mars\\anaconda3\\envs\\name1gpu\\lib\\site-packages\\ipykernel_launcher.py:13: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot a histogram of the emmissions per capita by country\n",
    "\n",
    "# subplots returns a touple with the figure, axis attributes.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.annotate(\"USA\",\n",
    "            xy=(18, 5), xycoords='data',\n",
    "            xytext=(18, 30), textcoords='data',\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc3\"),\n",
    "            )\n",
    "\n",
    "plt.hist(co2_2011['Value'], 10, normed=False, facecolor='green')\n",
    "\n",
    "plt.xlabel(stage['IndicatorName'].iloc[0])\n",
    "plt.ylabel('# of Countries')\n",
    "plt.title('Histogram of CO2 Emissions Per Capita')\n",
    "\n",
    "#plt.axis([10, 22, 0, 14])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the USA, at ~18 CO2 emissions (metric tons per capital) is quite high among all countries.\n",
    "\n",
    "An interesting next step, which we'll save for you, would be to explore how this relates to other industrialized nations and to look at the outliers with those values in the 40s!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.0em;color:blue; font-style:bold\">\n",
    "Matplotlib: Basic Plotting, Part 2</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between GPD and CO2 Emissions in USA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select GDP Per capita emissions for the United States\n",
    "hist_indicator = 'GDP per capita \\(constant 2005'\n",
    "hist_country = 'USA'\n",
    "\n",
    "mask1 = data['IndicatorName'].str.contains(hist_indicator) \n",
    "mask2 = data['CountryCode'].str.contains(hist_country)\n",
    "\n",
    "# stage is just those indicators matching the USA for country code and CO2 emissions over time.\n",
    "gdp_stage = data[mask1 & mask2]\n",
    "\n",
    "#plot gdp_stage vs stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>22282</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>GDP per capita (constant 2005 US$)</td>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>15482.707760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48759</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>GDP per capita (constant 2005 US$)</td>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>1961</td>\n",
       "      <td>15578.409657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CountryName CountryCode                       IndicatorName  \\\n",
       "22282  United States         USA  GDP per capita (constant 2005 US$)   \n",
       "48759  United States         USA  GDP per capita (constant 2005 US$)   \n",
       "\n",
       "        IndicatorCode  Year         Value  \n",
       "22282  NY.GDP.PCAP.KD  1960  15482.707760  \n",
       "48759  NY.GDP.PCAP.KD  1961  15578.409657  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_stage.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>22232</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1960</td>\n",
       "      <td>15.999779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48708</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1961</td>\n",
       "      <td>15.681256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CountryName CountryCode                           IndicatorName  \\\n",
       "22232  United States         USA  CO2 emissions (metric tons per capita)   \n",
       "48708  United States         USA  CO2 emissions (metric tons per capita)   \n",
       "\n",
       "        IndicatorCode  Year      Value  \n",
       "22232  EN.ATM.CO2E.PC  1960  15.999779  \n",
       "48708  EN.ATM.CO2E.PC  1961  15.681256  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# switch to a line plot\n",
    "plt.plot(gdp_stage['Year'].values, gdp_stage['Value'].values)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(gdp_stage['IndicatorName'].iloc[0])\n",
    "\n",
    "#label the figure\n",
    "plt.title('GDP Per Capita USA')\n",
    "\n",
    "# to make more honest, start they y axis at 0\n",
    "#plt.axis([1959, 2011,0,25])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So although we've seen a decline in the CO2 emissions per capita, it does not seem to translate to a decline in GDP per capita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ScatterPlot for comparing GDP against CO2 emissions (per capita)\n",
    "\n",
    "First, we'll need to make sure we're looking at the same time frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP Min Year =  1960 max:  2014\n",
      "CO2 Min Year =  1960 max:  2011\n"
     ]
    }
   ],
   "source": [
    "print(\"GDP Min Year = \", gdp_stage['Year'].min(), \"max: \", gdp_stage['Year'].max())\n",
    "print(\"CO2 Min Year = \", stage['Year'].min(), \"max: \", stage['Year'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 3 extra years of GDP data, so let's trim those off so the scatterplot has equal length arrays to compare (this is actually required by scatterplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "gdp_stage_trunc = gdp_stage[gdp_stage['Year'] < 2012]\n",
    "print(len(gdp_stage_trunc))\n",
    "print(len(stage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "axis.yaxis.grid(True)\n",
    "axis.set_title('CO2 Emissions vs. GDP \\(per capita\\)',fontsize=10)\n",
    "axis.set_xlabel(gdp_stage_trunc['IndicatorName'].iloc[0],fontsize=10)\n",
    "axis.set_ylabel(stage['IndicatorName'].iloc[0],fontsize=10)\n",
    "\n",
    "X = gdp_stage_trunc['Value']\n",
    "Y = stage['Value']\n",
    "\n",
    "axis.scatter(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't look like a strong relationship.  We can test this by looking at correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.07676005],\n",
       "       [0.07676005, 1.        ]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(gdp_stage_trunc['Value'],stage['Value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A correlation of 0.07 is pretty weak, but you'll learn more about correlation in the next course.\n",
    "\n",
    "You could continue to explore this to see if other countries have a closer relationship between CO2 emissions and GDP.  Perhaps it is stronger for developing countries?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Want more ? \n",
    "\n",
    "### Matplotlib Examples Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://matplotlib.org/examples/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.auto_scroll_threshold = 9999;\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.auto_scroll_threshold = 9999;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
